Artificial Electromagnetic Materials Research Articles

Deep Inverse Design of an Infrared Metasurface Diffuser

AbstractMachine learning (ML) algorithms have become invaluable tools for tackling design challenges associated with achieving unique scattering effects in artificial electromagnetic materials (AEMs). However, their effectiveness is reliant on substantial, well‐constructed training datasets. Building such datasets using traditional methods becomes impractical for increasingly complex and large‐scale geometric models. Achieving a specific diffuse scattering is one example and this often requires electrically large and diverse AEM arrays. Unfortunately, while numerical simulations offer high accuracy by utilizing fine meshing, their computational limitations render them incapable of handling such large structures and computing their scattering parameters efficiently. This work proposes a new approach to overcome these limitations by replacing conventional numerical simulations with a hybrid method that combines electromagnetic simulations with an analytical model, enabling the rapid and accurate generation of datasets for electrically large metamaterial arrays. Utilizing this approach, an optimized metasurface geometry for the mid‐infrared range is successfully identified and tested that exhibits desirable diffuse scattering effects. This innovative method paves the way for significantly faster design and optimization of metamaterials, while also unlocking the potential for a new generation of large‐scale, high‐quality ML datasets for AEM problems.

Terahertz metamaterials for spectrum modulation: structural design, materials and applications

Metamaterials, an artificial electromagnetic (EM) material, have exhibited unique characteristics, enabling innovations in terahertz (THz) wavefront shaping, polarization modulation, surface wave manipulation, and spectrum modulation. In this review, we focus on the advancements in THz metamaterials for spectrum modulation, emphasizing their structural design, special materials, and applications. The review discusses various structural designs, including metal-dielectric composite structures, all-metal structures, and all-dielectric structures, each offering distinct advantages for THz applications. Key special materials such as graphene, vanadium dioxide (VO2), molybdenum disulfide (MoS2), and flexible materials are highlighted for their significant contributions to enhancing the performance and functionality of THz metamaterials. The applications of THz metamaterials in EM stealth and sensing are thoroughly introduced, with a particular focus on biosensing, pesticide detection, and other sensing applications. The review emphasizes the potential of THz metamaterials in developing highly sensitive and selective sensors, as well as efficient THz absorbers and filters. Future perspectives include the continuous development of novel materials, advanced fabrication technologies, and the integration of multifunctional capabilities to further expand the applications and efficiency of THz metamaterials. These advancements are expected to drive significant progress in THz technology, impacting fields such as medical diagnostics, environmental monitoring, food safety, wireless communication, and security.

Microfluidic Metasurfaces: A New Frontier in Electromagnetic Wave Engineering

AbstractMetasurfaces, as 2D artificial electromagnetic materials, play a pivotal role in manipulating electromagnetic waves by controlling their amplitude, phase, and polarization. Achieving this control involves designing subwavelength meta‐molecules with specific geometries and periodicities. In the context of microfluidic metasurfaces, optical properties can be dynamically modulated by altering either the geometric structure of liquid meta‐molecules or the refractive index of the liquid medium. Leveraging the fluidity of liquid materials, microfluidic metasurfaces exhibit remarkable performance in terms of reconfigurability and flexibility. These properties not only establish a cutting‐edge research area but also broaden the scope of applications for active metasurface devices. Additionally, the integration of metasurfaces within microfluidic systems has led to novel functionalities, including enhanced particle manipulation and sensor technologies. Compared to conventional solid‐material‐based metasurfaces, microfluidic metasurfaces offer greater design freedom, making them advantageous for diverse fields such as electromagnetic absorption, optical sensing, holographic displays, and tunable optical meta‐devices like flat lenses and polarizers. This review provides insights into the characteristics, modulation techniques, and potential applications of microfluidic metasurfaces, illuminating both the current research landscape and promising avenues for further explorations.

DC-driven subatmospheric glow discharges in the infrared-stimulated

This paper presents a conceptual framework for experimental research combined with numerical analysis on direct current (DC) glow discharges in microscale planar gas discharge-semiconductor systems (GDSS). In the experimental section, several structural and elemental analyses, including SEM, EDAX, AFM, and near-infrared absorption spectra measurements were carried out for compound semiconductor zinc selenide (ZnSe) cathode sample. Argon (Ar) was charged into the plasma reactor cell of GDSS at pressures of 100 Torr subatmospheric and 760 Torr atmospheric, respectively, by a vacuum pump- gas filling station. Glow discharge light emissions from plasma, excited under three different intensity levels (dark, weak, strong) of infrared beam illumination on ZnSe cathode electrode, were measured by using a phomultiplier tube that is sensitive to UV–Visible wavelengths. In the numerical analysis section, simulation studies were carried out on the two-dimensional gas discharge-semiconductor microplasma system (GDSµPS) cell models using the finite-element method (FEM) solver COMSOL Multiphysics DC plasma program. Calculations and predictions were based on mixture-averaged diffusion drift theory and Maxwellian electron energy distribution function. GDSµPS cell was modeled in a square chamber with planar anode/cathode electrode pair coupled at a 50 μm discharge gap. Single side of ZnSe cathode was finely micro-digitated to increase the effective surface area for enhanced electron emission to the gas discharge cell. The electrical equivalent circuit (EEC) of the proposed model was driven by 1.0 kV DC voltage source. Binary Ar/H2 gas medium in a mixture of 3:2 molar ratio was introduced to the gas discharge chamber at constant 200 Torr subatmospheric pressure. Simulations were run for normal glow discharges to exhibit the electrical fast transient glow discharge behaviours from electron field emission state to self-sustained normal glow discharge state by numerically solving the electron density (ED), electron current density (ECD) and electric potential distribution (EPD) parameters.It is figured out that binary Ar/H2 gas discharge model can undertake a major role in shaping and controlling the spatiotemporal response to transient electro-optical behavior of microplasma-based artificial electromagnetic materials configured for high-efficiency infrared-to-visible wavelength conversion applications.

Ultra-Wideband and High-Gain Vivaldi Antenna with Artificial Electromagnetic Materials.

An ultra-wideband and high-gain Vivaldi antenna with artificial electromagnetic material, suitable for ground-penetrating radar (GPR) systems, is proposed. Directors loaded inside the antenna gradient slot direct electromagnetic waves by inducing current to improve gain. The artificial electromagnetic material, also called metamaterial, is composed of multiple "H"-shaped units arranged in a certain regular pattern, loaded at the antenna aperture. The artificial electromagnetic units affect the antenna radiation waves by changing the refractive index to improve radiation directivity. The four Vivaldi units are arranged into a horn-shaped array, and each two units are orthogonally fed to realize dual polarization. Experimental results demonstrate that the antenna has good impedance matching of S11≤-10 dB in 0.9-4 GHz, and the maximum realized gain can reach 15.2 dBi.

A High Gain Vivaldi Antenna with Multiple Near-Field Dielectric Lenses and Grooved Edges

In order to expand the applications of the traditional Vivaldi antenna by addressing the low gain of the traditional Vivaldi antenna, this paper combines the phase distribution of the Vivaldi antenna aperture based on the analysis of the transmission characteristics of a dielectric unit by utilizing the advantages of artificial electromagnetic material. A more reasonable method of loading a dielectric lens is proposed and utilized in combination with edge grooving. A comparison of the results shows that the antenna operates in the frequency range of 4 to 16 GHz. The low-frequency boost is approximately 3 dB, and the high-frequency boost is approximately 9.6 dB.

Toroidal dipoles in tunable THz metamaterial based on perovskite

In recent years, there has been an increasing variety of researches and related applications on terahertz (THz). It is a new type of radiation source whose radiation lies in the so-called terahertz gap. However, due to the limitations of natural materials, the terahertz band rarely has effective response. Metamaterials, which have emerged in recent years, are artificial composite electromagnetic materials composed of periodic elements. In the terahertz band, it has special physical properties not found in natural materials. Toroidal dipoles can be detected by modeling tunable terahertz metamaterials. A tunable toroidal dipole device based on a perovskite terahertz metamaterial combining perovskite with organic and inorganic hybrid halide perovskite films is proposed. The optical parameters of perovskite can be changed from toroidal dipole to magnetic dipole and electric dipole by pumping light. This research is of great significance to the design of terahertz devices such as filters and modulators.

Deep inverse photonic design: A tutorial

Deep learning (DL) has been used to design deep neural networks (DNNs) which have recently been applied to solving inverse problems in artificial electromagnetic materials (AEMs). Although inverse problems are often ill-posed, and therefore are difficult to solve, deep inverse models (DIMs) have achieved impressive results often surpassing capabilities possible with other approaches. We overview the process of deep inverse learning applied to AEM problems, including the building of data sets, design of a forward model, and comparison of inverse approaches including limitations. We conclude by detailing some important outstanding issues of deep inverse design of AEMs, and present an outlook of this exciting field.

Harnessing DNA Nanotechnology and Chemistry for Applications in Photonics and Electronics.

Many photonic and electronic devices rely on nanotechnology and nanofabrication, but DNA-based approaches have yet to make a significant commercial impact in these fields even though DNA molecules are now well-established as versatile building blocks for nanostructures. As we describe here, DNA molecules can be chemically modified with a wide variety of functional groups enabling nanocargoes to be attached at precisely determined locations. DNA nanostructures can also be used as templates for the growth of inorganic structures. Together, these factors enable the use of DNA nanotechnology for the construction of many novel devices and systems. In this topical review, we discuss four case studies of potential applications in photonics and electronics: carbon nanotube transistors, devices for quantum computing, artificial electromagnetic materials, and enzymatic fuel cells. We conclude by speculating about the barriers to the exploitation of these technologies in real-world settings.

Imaging Properties of Nonperiodic Time-Varying Active Frequency Selective Surface

The rapid development of artificial electromagnetic (EM) materials provides unprecedented freedom to manipulate EM waves. In recent years, reflective spectrum conversion technology based on EM materials has been widely studied due to its multiharmonic generation capability. The possibility of its application in wireless communication is explored. However, the research on spectrum conversion is mainly based on periodic modulation waveforms. The modulation effect of nonperiodic for EM materials has not been studied so far. In this article, a nonperiodic time-varying active frequency selective surface (AFSS) is proposed to manipulate the spectral distribution of harmonics. By dynamically controlling the amplitude of the reflected echo, a continuous Doppler spectrum is generated. On this basis, the imaging characteristics of nonperiodic time-varying AFSS are analyzed in detail, and the defocusing phenomenon of the target image is discovered. Finally, the radar imaging experiments for nonperiodic time-varying AFSS are conducted to verify the validity of the proposed method.

Multi-functional thermal-mechanical anisotropic metasurface with shape memory alloy actuators

Metasurfaces are two-dimensional (2D) artificial electromagnetic (EM) materials comprising subwavelength periodic or non-periodic arrays of resonator elements. Multi-functional metasurfaces have shown magnificent potentials for many applications, including wireless communication, radar systems, and security. EM functions of metasurfaces can generally be switched or controlled by electrical or optical components, but these conventional tuning technologies suffer from complexity, high cost, and design limitations for large scales and function integration. Therefore, this paper proposes a novel thermal–mechanical anisotropic metasurface to provide multiple functionalities through mechanical transformation due to thermal stimulus on two shape memory alloy actuators, switching EM functionality between absorption and reflection. The absorptivity and reflectivity at 10.5 GHz are 96.5% and 81.33%, respectively. It consists of the motional metasurface and stational metasurface where mechanical transformation is achieved by the motional metasurface. In addition to the mechanical transformation, its function is switched depending on incident EM wave direction due to anisotropy that could achieved the transmission enhancement in ratio of 11.02 dB at operation frequency 5.9 GHz in opposing excitation. The proposed idea is numerically and experimentally demonstrated and discussed.

In‐bandscattering reduction of phased array by loading artificial electromagnetic materials

A new approach based artificial electromagnetic materials (AEMMs) with low transmission loss is proposed for phase array scattering cross section (SCS) reduction. By putting an AEMM unit with different transmission phase above a phased array element, a new array element with different scattering phase can be constructed. Then, a low-SCS array aperture can be synthesized based on the new element by taking advantage of its tunable scattering phase. An 8 × 8 array prototype is fabricated to validate the radiation and scattering characteristics of the proposed low-SCS phased array. The measured results indicate that its SCS is controlled in the operating bandwidth of the phased array antenna with an acceptable degradation of radiation characteristics.

Inverse deep learning methods and benchmarks for artificial electromagnetic material design.

In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) - such as metamaterials, photonic crystals, and plasmonics - to achieve some desired scattering properties (e.g., transmission or reflection spectrum). DIMs are deep neural networks (i.e., deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. We publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.

Classification and Inverse Design of Metasurface Absorber in Visible Band

AbstractMetasurface absorber (MA) has been a research hotspot in the field of artificial electromagnetic structural material due to its dual advantages of high performance and compact design. Usually, the design of MA depends on the designer's professional knowledge, experience and physical inspiration. The desired optical response can be obtained by using electromagnetic simulation software to carry out hundreds or thousands of numerical calculations. Thus, it is still a challenge to quickly retrieve the optimal structure according to the desired optical response and realize the on‐demand inverse design. Besides, limited by the inner physics of the MA, it is not always possible to find the structural parameters corresponding to the desired spectrum. This paper not only takes the planar geometry and thickness of the structures into account but also realizes the probability classification of the desired spectra through the classification network. According to the classification results, the prediction network of the corresponding is selected to realize the on‐demand inverse design of MA. The proposed network model can design MA rapidly and accurately in a data‐driven way and can be flexibly applied to the design of other data‐enabled photonic devices, which is promising to become a comprehensive and effective design tool.

Surface Nano-Patterning for the Bottom-Up Growth of III-V Semiconductor Nanowire Ordered Arrays.

Ordered arrays of vertically aligned semiconductor nanowires are regarded as promising candidates for the realization of all-dielectric metamaterials, artificial electromagnetic materials, whose properties can be engineered to enable new functions and enhanced device performances with respect to naturally existing materials. In this review we account for the recent progresses in substrate nanopatterning methods, strategies and approaches that overall constitute the preliminary step towards the bottom-up growth of arrays of vertically aligned semiconductor nanowires with a controlled location, size and morphology of each nanowire. While we focus specifically on III-V semiconductor nanowires, several concepts, mechanisms and conclusions reported in the manuscript can be invoked and are valid also for different nanowire materials.

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

AbstractDeep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are research fields where DNN results valorize the data driven approach; especially in cases where conventional methods have failed. In view of the great potential of deep learning for the future of artificial electromagnetic materials research, the status of the field with a focus on recent advances, key limitations, and future directions is reviewed. Strategies, guidance, evaluation, and limits of using deep networks for both forward and inverse AEM problems are presented.

Neural-adjoint method for the inverse design of all-dielectric metasurfaces

All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields a desired spectra remains largely unsolved. We propose and demonstrate a method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic crystal, and other artificial electromagnetic materials.

Ultrathin Perfect Metamaterial Absorber Based on Triquetra Shape

Electromagnetic Metamaterial perfect absorber EM-MMPA is representative an artificial electromagnetic material, which has many application prospects in huge fields like energy harvesting from electromagnetic wave. This work focused on the design and simulation ultrathin structure based on polarization unsensitive. However, based on the three-layer structure of copper reflector, dielectric layer and copper triquetra resonator. Through the results, it is found that, at 10.026 GHz with the fundamental mode of resonance can achieve a polarization unsensitive perfect absorption. Furthermore, all the materials involved in this work are easily available, and the size of the unit cell is also within the range of easy implementation, and it has a good prospect of many numbers of practical application.

Novel materials in terahertz functional devices

Terahertz (THz) science and technology have been developed rapidly over the past decade due to its superiority in security, communication, imaging, and spectroscopy. In order to manipulate THz waves efficiently, many THz functional materials and devices have been proposed. Metasurfaces with subwavelength elements arranged in a periodic or quasi-periodic manner have been widely investigated. The amplitude, phase and polarization state can be controlled flexibly by designing the geometry. In this paper, several typical THz electromagnetic functional materials will be introduced, magnetic-optical semiconductors, nanoparticle liquid crystal, 3D graphene foam, carbon nanotubes, etc. These electromagnetic functional materials show unique functions for THz active modulation, polarization conversion, one-way transmission, and perfect absorption. Combined with these new materials, we designed and fabricated a series of THz metasurface device to enhance or expand the functions of these functional materials. Meanwhile, the introduction of functional materials brings THz metasurfaces into the active properties. The combination of these artificial micro-structures and electromagnetic functional materials bring new development for active or multifunctional THz devices.

S/X dual-band real-time modulated frequency selective surface based absorber

Frequency selective surface (FSS) is of great research interest for its wide applications in radome, absorber, electromagnetic filters, and artificial electromagnetic bandgap materials. In order to achieve a multifunctional FSS with real-time manipulated radar cross-section (RCS), there are mainly three ways, i.e. to design reconfigurable FSS unit cell, reconfigurable screen, and a combination of reconfigurable unit cell and screen. In this work, a combination design of both the reconfigurable unit cells and FSS screen is proposed to realize a dual-band FSS absorber with real-time manipulated RCS. For the reconfigurable unit cell, an angular ring and a meander cross dipole are combined to obtain a dual-band absorption. The dual-band resonance frequencies are reconfigurable by switching the PIN diodes embedded in the unit cell. When switching the PIN diodes, the resonance frequencies of the unit cell would be changed due to the variation of the effective capacitance and inductance of the unit cell. For the reconfigurable FSS screen, a novel biasing network is introduced, then the scattering field from each unit cell is modulated independently by switching the “on/off” state of the PIN diode through using a programmable field programmable gate array (FPGA). The total scattering far field is expressed as the superposition of the scattering field from each unit cell, and the far field scattered by the unit cell which is evaluated under an infinite periodic boundary condition. The scattering field of the FSS absorber can be predicted by considering the working states of all the unit cells on the screen. We define the unit cell as state “0”, when all the PIN diodes are at the states of “off”, and as state “1” when the PIN diodes are all at the states of “off”. The entire screen of FSS absorber is thus pixelated, which can be expressed by a binary coding matrix. The real-time scattering fields from the FSS absorber are manipulated perfectly by optimizing the states matrices showing “on/off” of each unit cell with genetic algorithm (GA). The FSS absorber is fabricated and measured. The ranges of 33dB and 25dB reconfigurable monostatic RCS at 3.2 GHz and 10.3 GHz are achieved by coding the states of unit cells on the FSS absorber screen. Both full-wave and analytical simulations demonstrate the effectiveness of the proposed optimization procedure. Compared with the reported FSS absorber, the proposed design is validated to possess good performance.